Web sites and other types of interactive systems may implement recommendation services for recommending items stored or represented in a data repository. These services can operate, for example, by receiving an input list of items (optionally with associated item weights), and by outputting a ranked list of items that are collectively similar or related to the input set. The items included in the input set are referred to herein as “source items” or “sources.”
One common application for recommendation services involves recommending products for purchase, rental, subscription, viewing or some other form of consumption. For example, e-commerce web sites commonly provide services for recommending products to users based on their respective purchase histories, rental histories, product viewing histories, and/or item ratings. Recommendation services are also commonly used to recommend web sites, articles, users, music and video files, and other types of items.
When generating recommendations for a particular user (referred to herein as the “target user”), the set of source items should ideally consist of items the target user likes. Otherwise, the recommendations may be of limited utility. Unfortunately, the task of reliably identifying such items without requiring explicit user input can be difficult. For example, although a user's purchase history as maintained by an e-commerce web site is typically very useful for generating recommendations, this purchase history may include items purchased by the user for others as gifts. Unless the user actually designated these items as gifts at the time of purchase, these items may be difficult to identify and filter out. As another example, the purchase history may include purchases made by multiple family members that share a home computer and account. The task of identifying appropriate source items is similarly difficult when the recommendations are based, e.g., on the item viewing histories, item rental histories, or item download histories of users.
To address this problem, some web sites allow users to view and “edit” their respective purchase histories, item viewing histories, and/or other item collections on an item-by-item basis, such as by rating, deleting, and/or tagging particular items. These edits are then taken into consideration in generating recommendations for the user. As one example, a user may delete from his or her purchase history all gift purchases, or may otherwise mark these items to indicate that they should not be used to generate recommendations. As another example, a user may tag the purchases that correspond to a particular family member or interest, and then request tag-specific recommendations that are based specifically on those purchases. In addition, some systems enable users to explicitly rate individual items that are recommended to them as “not interested.”
While these “collection management” features can significantly improve the quality of the recommendations, many users do not take the time to review and manage their respective collections of items. Indeed, the task of reviewing and editing purchase histories and other item collections on an item-by-item basis can be burdensome. In addition, a user's interests might change over time, rendering some of the past item ratings inaccurate, for example, items rated by a user as “not interested” one year ago may not be relevant today. For these and other reasons, many users continue to receive recommendations that are not sufficiently tailored to their respective interests.